Suppr超能文献

不同类型的先验知识和任务属性如何影响基于类别推理:来自 P2、N400 和 LPC 效应的不同证据。

How types of prior knowledge and task properties impact the category-based induction: diverging evidence from the P2, N400, and LPC effects.

机构信息

School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; College of Psychology, Shenzhen University, Shenzhen, 518060, China.

Department of Education Science, Innovation Center for Fundamental Education Quality Enhancement of Shanxi Province, Shanxi Normal University, Linfen 041000, China.

出版信息

Biol Psychol. 2020 Oct;156:107951. doi: 10.1016/j.biopsycho.2020.107951. Epub 2020 Sep 3.

Abstract

Category-based induction task was combined with ERP to unravel whether prior knowledge and property interact when inferring on genes or diseases. Larger P2 amplitudes for near taxonomic/causal distances relative to far ones, as well as larger LPC for taxonomic relation relative to thematic relation, are found in both gene and disease tasks. However, smaller N400 is found for taxonomic relation in gene task and thematic relation in disease task, respectively, and larger LPC at 700-850 ms for near taxonomic distance in the gene task and near causal distance in the disease task. These results suggested that the category-based inductive reasoning is context-sensitive, and there may be four stages of category-based inductive reasoning: the early automatic comparison of features/relations (P2), features/relations generalization process (N400), the extraction of common relationship/rule (LPC at 550-700 ms), the inference generation (LPC at 700-850 ms).

摘要

基于类别归纳任务与 ERP 相结合,以探究在推断基因或疾病时,先验知识和属性是否会相互作用。在基因和疾病任务中,都发现了近分类/因果距离的 P2 振幅较大,而分类关系的 LPC 较大,主题关系的 LPC 较大。然而,在基因任务中,分类关系的 N400 较小,在疾病任务中,主题关系的 LPC 较大,在基因任务中,近分类距离的 LPC 在 700-850 ms 较大,在疾病任务中,近因果距离的 LPC 在 700-850 ms 较大。这些结果表明,基于类别归纳推理是上下文敏感的,可能存在四个阶段的基于类别归纳推理:特征/关系的早期自动比较(P2)、特征/关系的概括过程(N400)、共同关系/规则的提取(550-700 ms 的 LPC)、推理生成(700-850 ms 的 LPC)。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验